Distributed Reconciliation in Day-Ahead Wind Power Forecasting

Li Bai, Pierre Pinson*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

293 Downloads (Pure)


With increasing renewable energy generation capacities connected to the power grid, a number of decision-making problems require some form of consistency in the forecasts that are being used as input. In everyday words, one expects that the sum of the power generation forecasts for a set of wind farms is equal to the forecast made directly for the power generation of that portfolio. This forecast reconciliation problem has attracted increased attention in the energy forecasting literature over the last few years. Here, we review the state of the art and its applicability to day-ahead forecasting of wind power generation, in the context of spatial reconciliation. After gathering some observations on the properties of the game-theoretical optimal projection reconciliation approach, we propose to readily rethink it in a distributed setup by using the Alternating Direction Method of Multipliers (ADMM). Three case studies are considered for illustrating the interest and performance of the approach, based on simulated data, the National Renewable Energy Labaratory (NREL) Wind Toolkit dataset, and a dataset for a number of geographically distributed wind farms in Sardinia, Italy.
Original languageEnglish
Article number1112
Issue number6
Number of pages19
Publication statusPublished - 2019


  • Wind energy
  • Hierarchical time-series
  • Forecast reconciliation
  • Distributed optimization


Dive into the research topics of 'Distributed Reconciliation in Day-Ahead Wind Power Forecasting'. Together they form a unique fingerprint.

Cite this